This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Analytics Engineer based in Canada.
This is an opportunity to build and scale the analytical foundation behind critical business and product decisions in a high-growth SaaS environment.
You will work at the intersection of data engineering, analytics, and business strategy, transforming complex data into trusted, actionable insights.
Your work will support teams across Product, Go-to-Market, Finance, People, Marketing, and Operations.
You will own key components of a modern analytics stack, including dbt, Snowflake, Python, Airflow, data pipelines, and semantic layers.
The role also offers the opportunity to build AI-ready data infrastructure that enables AI agents and LLM-powered tools to answer business questions reliably.
You will have significant ownership over data quality, governance, observability, and measurement frameworks.
This is a highly collaborative environment where your technical expertise will directly influence product decisions, operational efficiency, and business growth.
Accountabilities:
- Own and continuously evolve the dbt analytics environment, ensuring models are performant, tested, documented, and aligned with modern data modeling practices.
- Design, maintain, and optimize Snowflake data warehouse structures and data ingestion processes.
- Develop core entities and datasets that accurately represent complex business processes, metrics, and operational logic.
- Build and maintain Python and Airflow pipelines for ingesting data from third-party APIs into the cloud data warehouse.
- Design cross-system reconciliation models to identify discrepancies, protect revenue, and improve data consistency across multiple source systems.
- Establish robust testing, observability, CI/CD, linting, code review, and approval practices for analytics pipelines.
- Standardize metric definitions and ensure consistent calculations across dashboards, analytics tools, and business functions.
- Investigate data incidents from root cause through remediation, documentation, and stakeholder communication.
- Partner with Engineering, Product, Marketing, RevOps, Finance, and People teams to align data definitions, instrumentation, and analytical requirements.
- Enable stakeholder self-service by providing trusted datasets, metrics, and guidance on effective querying, dashboarding, and data interpretation.
- Promote data literacy and coach business stakeholders on analytics best practices.
- Design and maintain governed semantic views that provide reliable interfaces between business data and AI agents or LLM-powered applications.
- Collaborate with AI and product teams to define, implement, and validate semantic layers supporting internal AI assistants.
- Develop measurement frameworks for AI-powered initiatives, including experiment design, attribution, and impact measurement.
- Proactively identify data discrepancies, quantify their business impact, and coordinate resolution with operational teams.
- Define measurement approaches for new initiatives, establishing success criteria and tracking requirements before launch.
Requirements:
- 5+ years of professional experience as an Analytics Engineer, Data Engineer, or in a similar role, preferably within a SaaS environment.
- Deep expertise in SQL, dbt, and modern data modeling principles.
- Strong Python skills for pipeline development, API integrations, automation, and data processing.
- Experience modeling Salesforce data, including opportunities, contracts, subscriptions, cases, and field history.
- Proven experience developing custom ELT pipelines that ingest third-party API data into cloud data warehouses.
- Experience designing reconciliation models that join, deduplicate, compare, and validate data across multiple source systems.
- Hands-on experience with event-based and product usage data, using tools such as PostHog or Mixpanel.
- Experience connecting marketing data—including paid advertising, campaigns, and attribution—to product analytics and downstream conversion and retention metrics.
- Experience designing and maintaining governed semantic layers, such as dbt Semantic Layer, Snowflake Cortex, or comparable technologies.
- Strong familiarity with large-scale cloud data platforms such as Snowflake, BigQuery, or Redshift.
- Experience with Git-based development workflows, CI/CD, automated testing, and data quality practices.
- Experience collaborating effectively with engineers, analysts, product managers, and business stakeholders.
- Demonstrated ability to use analytics to influence decisions in technical, product, or business environments.
- Strong ownership mindset and the ability to independently navigate ambiguous problems and design end-to-end solutions.
- Experience with Airflow DAGs and multi-source API orchestration is a plus.
- Knowledge of statistics and experimentation, including A/B testing, significance testing, and incremental impact measurement, is desirable.
- Familiarity with predictive modeling concepts such as classification, feature selection, and model evaluation is an advantage.
- Understanding of financial SaaS metrics and billing processes, including ARR, MRR, NRR, subscription reconciliation, and revenue recognition, is a plus.
- Experience with people analytics, including headcount, attrition, and compensation benchmarking, is beneficial.
Benefits:
- Competitive compensation and benefits package.
- Opportunity to work in a high-growth SaaS and technology environment.
- Significant ownership over critical analytics infrastructure and data products.
- Opportunity to work with modern data technologies including Snowflake, dbt, Python, Airflow, and AI-enabled semantic layers.
- Exposure to AI-powered analytics and infrastructure supporting LLMs and autonomous data experiences.
- Cross-functional collaboration with Product, Engineering, Marketing, Finance, People, RevOps, and Operations teams.
- Opportunity to influence strategic decisions through trusted data and measurement frameworks.
- Remote-friendly work environment in Canada.
- Inclusive and collaborative culture focused on innovation, ownership, and continuous improvement.
- Equal opportunity workplace committed to considering qualified candidates fairly.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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